similar to: Passing data to aov

Hello List Folks. I?m trying to do manova using a function which assembles the response (LHS) and factors (RHS) from different sources. When I do so, the model routines complain that I'm passing a list for the RHS when I don't think I am. Here's a toy example: r <- matrix(rnorm(30), ncol = 3) # toy data td <- list(d = rnorm(10), f1 = sample(c("A",

Hi, Suppose I have: # Fit a base model d1.ph <- coxph(Surv(start, stop, event)~ ejec + diavol + score + smoking + beta + surg.done, data = data.frame(foo)) summary(update(d1.ph, . ~ . + td1)) summary(update(d1.ph, . ~ . + td2)) As I have many columns in my data frame, foo, called td's. e.g. td1, td2, td3, .... And I'd like to

Hello All. I must be missing the really obvious here: mm <- function(d, i) median(d[i]) b1 <- boot(gravity$g, mm, R = 1000) b1 b2 <- boot(gravity$g, mm, R = 1000, strata = gravity$series) b2 Both b1 and b2 seem to have done (almost) the same thing, but it looks like the strata argument in b2 has been ignored. However, str(b1) vs str(b2) does show that the strata have been noted

I've done a decent size update of the OpenSSH portable from the upstream tree. There is an AES upgrade that needs to take place, but I need people to test and tell me what endedness changes need to be applied (if any). Attached is the patch. It is geared towards the latter snapshots..Unsure how it will patch (if at all) against 2.9pX series. Thanks. - Ben "This is dark day in

Hola: Probablemente está tomando 'Edad_Manova_18a54' y 'Genero2_1a2' como variables numéricas (integer). Deberías convertirlas en factor. Por ejemplo: Edad_Manova_18a54 <- factor(Edad_Manova_18a54) Genero2_1a2 <- factor(Genero2_1a2) Manova.fnc(datosPAS, variables=2:6, fac.inter=c('Edad_Manova_18a54','Genero2_1a2'),poshoc=c('todos')) Saludos,

Hello All... I gave a task to my students that involved using mclust to look for clusters in some bivariate data of isotopes vs various mining locations. They discovered something I didn?t expect; the data (called tur) is appended below. p <- qplot(x = dD, y = dCu65, data = tur, color = mine) print(p) # simple bivariate plot of the data; looks fine mod1 <- Mclust(tur[,2:3]) mod1$G mod2

Muy buenas, He usado muy poco R en mi vida, y ahora estoy intentando hacer una MANOVA, con dos variables independientes (Edad, que tiene 4 niveles y Genero que tiene 2) y 5 variables independientes. Le he puesto también que me haga el poshoc de las dos y la interacción. Decir también que me instalé el ULLRToolbox, que no se si tendrá algo que ver con el error que me da. Este es el código que uso

Folks, I have Googled but not found much regarding arrayInd aside from the "which" help page. Any good examples or docs on what arrayInd does that is better or different from which()? In addition take the following 20x10 matrix: td<-structure(c(1, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 1, 6, 6, 6, 6, 6, 6, 6, 1, 6, 6, 6, 6,

Hi, I need some help again. :-) I have "Too much recursion error" prototype.js line 1288, I heve this error when I try to use Effect.dropout (script.aculo.us) if I remove table ID, there is no error, and the effect works fine (remove the tr from table) But I need this ID assign to this table, as table ID is used by other effect (Builder.node) Please help .... Thanks YUAN Here is

Hi, I would like to conduct a MANOVA. I know that there 's the manova() funciton and the summary.manova() function to get the appropriate summary of test statistics. I just don't manage to specify my model in the manova() call. How to specify a model with multiple responses and one explanatory factor? If I type:

Hello, I had a couple questions about manova modeling in R. I have calculated a manova model, and generated a summary.manova output using both the Wilks test and Pillai test. The output is essentially the same, except that the Wilks lambda = 1 - Pillai. Is this normal? (The output from both is appended below.) My other question is about the use of MANOVA. If I have one variable which has a

Can R do a repeated measures MANOVA and tell what dimensionality the statistical variance occupies? I have been using MATLAB and SPSS to do my statistics. MATLAB can do ANOVAs and MANOVAs. When it performs a MANOVA, it returns a parameter d that estimates the dimensionality in which the means lie. It also returns a vector of p-values, where each p_n tests the null hypothesis that the mean

Hi all, I am experimenting the function "manova" in R. I tried it on a few data sets, but I did not understand the result: I used "summary(manova_result)" and "summary(manova_result, test='Wilks')" and they gave a bunch of numbers... But I need the Sum-of-Squares of BETWEEN and WITHIN matrices... How do I read off from the R's manova results? Any

Hi there, Does anyone know how to extract elements from the table returned by Manova()? Using the univariate equivalent, Anova(), it's easy: a.an<-Anova(lm(y~x1*x2)) a.an$F This will return a vector of the F-values in order of the terms of the model. However, a similar application using Manova(): m.an<-Manova(lm(Y~x1~x2)) m.an$F Returns NULL. So does any attempt at calling the

Hi all, I have received the following error from summary.manova: Error in summary.manova(manova.test, test = "Pillai") : residuals have rank 36 < 64 The data is simulated data for 64 variables. The design is a 2*2 factorial with 10 replicates per treatment. Looking at the code for summary.manova, the error involves a problem with qr(). Does anyone have a suggestion as to how to

Dear friends, Sorry for this somewhat generically titled posting but I had a question with using contrasts in a manova context. So here is my question: Suppose I am interested in doing inference on \beta in the case of the model given by: Y = X %*% \beta + e where Y is a n x p matrix of observations, X is a n x m design matrix, \beta is m x p matrix of parameters, and e is a

While I was helping a SAS-using friend with an analysis I noticed some differences in the multivariate test statistics, approximate F statistics, and p-values in the manova function using R and proc GLM using SAS. The univariate coefficients are identical. Is there a reason to expect R and SAS to give different results? Thanks, Bill Kristan.

Hi, I have problem getting the standard deviation from the manova output. I have used the manova function: myfit <- manova(cbind(y1, y2) ~ x1 + x2 + x3, data=mydata) . I tried to get the predicted values and their standard deviation by using: predict(myfit, type="response", se.fit=TRUE) But the problem is that I don't get the standard deviation values, I only

I'm trying to learn to use manova(), and don't understand why none of the following work: > data(iris) > fit <- manova(~ Species, data=iris) Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : incompatible dimensions > fit <- manova(iris[,1:4] ~ Species, data=iris) Error in model.frame(formula, rownames, variables, varnames, extras,

Dear all, I need to do a Manova but I have an unbalanced design. I have morphological measurements similar to the iris dataset, but I don't have the same number of measurements for all species. Does anyone know a procedure to do Manova with this kind of input in R? Thank you very much, Naiara. -------------------------------------------- Naiara S. Pinto Ecology, Evolution and Behavior 1